DESCRIPTION (Abstract Provided by Applicant): Remarkably little is known about protective factors that reduce African Americans' risk for depression and diabetes comorbidity. In particular, the link between service use and risk reduction in African Americans has never been demonstrated with a nationally representative sample, particularly one that also takes into account ethnic groups' differences. The lack of research on depression and diabetes comorbidity research among African Americans is due primarily to rather small samples of Black Americans in available data sets from which to draw meaningful conclusions. The proposed research plan aims to provide a comprehensive examination of whether socioeconomic status (SES) and known risk and protective factors are correlated with reductions in the risk for comorbid depression and diabetes with special attention to low income populations in a nationally representative sample of 5,191 African Americans and Caribbean Blacks. The public health significance lies in specifying which demographic factors related to lower comorbidity risk, which will inform subsequent health social service prevention interventions and strategies. The present study plan is designed to advance knowledge and make five important contributions to the literature on depression and diabetes comorbidity among African Americans, namely by: (1) capitalizing on the strengths of a large, nationally representative data set, the National Survey of American Life [NSAL]; (2) determining whether SES is associated with reductions in the risk for the studied comorbid condition by Blacks; (3) examining the mediation effects of health insurance status on the relationship between SES and comorbid depression and diabetes conditions; (4) investigating the relative effects of comorbid status on employment outcomes for welfare recipients; and (5) describing the cultural meaning of being diagnosed with and managing health care with a comorbid diagnosis. The data analytic approaches to be used include logistic regression, multi-nominal logistic regression, and qualitative theme analyses techniques.